Each single-step model is evaluated by measuring its accuracy and inference time. Single-step accuracy [1]} is defined as the percentage of target compounds for which the model finds the ground-truth reactants within the top-k, \(k \in \lbrace 1,3,5,10,50\rbrace \) , measuring the ability of the model to capture chemical reaction information. Inference time is defined as the time needed to produce retrosynthesis predictions for a set of molecules, measuring the ability of the model to provide predictions in a timely manner. In an ablation study, we measure the impact of the amount of evaluation data and batch size on the inference time. For the first, we measure the influence of doubling the evaluation data while using the default batch size (Appendix REF ), analyzing the scalability of the model. For the second, we measure the impact of setting the batch size to 1, replicating the necessary conditions for a multi-step search algorithm that can only explore one molecule per instance (e.g. [2]}).
